Education and Health Policies

Eliana La Ferrara May 2016

1. Progresa/Oportunidades (Schultz, 2004) PROGRESA = Spanish Acronym of « Health,

Nutrition & Education » 3 components

Largest component : Education Important welfare program reduce poverty in rural Mexico Initiated in 1997 by Mexican government Similar programs in Brazil, Honduras,

Nicaragua, Columbia, Turkey, Argentina…

Scale of the Program

End of 1999 implemented in more than 50,000 communities

Covered about 2.6 millions households = 40% of all rural families

Budget = 1billion US$ First welfare program to survive a change of

administration in Mexico (though changed name into “Oportunidades”!)

Has been expanded to poor urban areas

Welfare programs

Usually, anti-poverty programs : = Transfer to people below a specified level

of income Negative effects : Distortion of private resource allocation ↓ incentive to work Rent seeking

Welfare programs

Education programs - supply approach (e.g., building schools) - demand approach (e.g., subsidies to families) BUT Progresa Program is ≠ : Minimize the disincentives to work by not

conditioning transfers on incomes Target poor households directly and exclusively Not permanent ( less distortion)

Progresa grants Promised for only 3 years Conditional on school attendance (85% of time), as

reported by teacher Increase with grade Received by the mother Premium for girls (secondary school) Maximum 625 pesos/hh per month = 52% of

average beneficiary’s income 2/3 of what a child could earn if working full time

Conditions for qualification

Objective : Targeting the poorest

1.“Regional qualification” 2 Criteria to be eligible :

High poverty (high index of marginalization) Access to certain basic structures (schools,

health centers…) 495 poor rural localities identified

2.“Private Qualification” Individual households qualify if latent poverty index high enough (income, house type, running water…) 2/3 of households qualify for the program

Conditions for qualification

Progresa randomization

Among poor eligible localities (495): 2/3 randomly selected (314) to receive the program during the first 2 years Treatment localities (Progresa localities) Remaining 1/3 (181) receive only the program in the third year Control localities (non Prog. localities)

Treatment and control states

Treatment States 1. Guerrero 2. Puebla 3. Veracruz 4. Hidalgo Control States 1. Michoacan 2. Queretaro 3. San Luis Potosi

Data collection

Sample: households belonging to eligible and non-eligible localities.

5 rounds of data Panel sample of children :

from 6 to 16 years old from grade 1 to 9

Enrollment model

iPiS

iE

kC

Probability of being enrolled in school

Residing in a Progresa locality

Eligibility for Progresa benefits

*** program impact ***

K Completed years of schooling

Control variables

ii EP

jiX

Estimated post-program effects

Separate estimation :

• For boys and girls

• For the two school levels

Diff-in-diff method

Impact of PROGRESA on enrollment

Difference in difference estimator

For children who qualify to enroll in junior secondary, enrollment ↑ by 11.1 pct points

Impact greatest for girls:

14.8 pct points

Inequality: Tested hypothesis

)()(2 ,2,4,1,3 ttttt SSSSD −−−=

)]()[()()(2 1,21,41,11,3,2,4,1,3 −−−− −−−−−−= ttttttttt SSSSSSSSDD

Impact of PROGRESA on inequality in enrollment

Difference in difference estimator

Tested hypothesis

Impact of PROGRESA on inequality in enrollment

Public costs vs. private benefits

Progresa ↑ 0,66 years of schooling Impact on expected wages: ↑ 1 year of secondary school ↑ 12% ↑ 1 year of primary school lower

Internal rate of return on private wages = 8%

per year

How do the benefits compare to other interventions in education?

Progresa: extremely influential model, especially in Latin America

But very expensive and demanding in terms of admin capacity

J-PAL researchers have compared effects of

alternative interventions in education

How to increase child attendance?

Source: J-PAL

2. School construction in Indonesia Duflo (AER 2001)

Two questions Does investment in infrastructure lead to increased

educational attainment? (supply side) Does increased educational attainment cause

higher earnings?

Returns to schooling: why not enough to estimate an OLS regression of wages on schooling?

Omitted variables: ability, family background, … Solution proposed in this paper: school construction

generates exogenous variation in schooling

The INPRES program 1973-79: > 60,000 primary schools constructed

(doubled stock of schools) Oil revenues mobilized to finance this program (> 5

billion USD = 1,5% Indonesian GDP) Gov’t recruited & trained additional teachers

Allocation rule of schools to districts Goal: reduce inequality across regions

# schools constructed ∝ # children of primary school age not enrolled in 1972

Increased enrollment for children aged 7-12: from 69% in 1973 to 83% in 1978

Identification Can we estimate effect by comparing enrollment in regions w/ & w/o school construction? before & after school construction? Idea underlying DiD: individual exposure to the program is determined by: region of birth: construction based on enrollment

rate in 1972 date of birth: children born ≤1962 were ≥12 yrs

old in 1974 no benefit. For children born after 1962, exposure ↑ w/ date of birth.

DiD strategy

Compare enrollment rates for individuals born in cohorts exposed vs non-exposed to program in regions w/ vs w/o school construction

Identifying assumption?

In the absence of program, the change in educational attainment would not have been systematically different in low and high program regions

Results (1): years of education 2 x 2 table

Std. errors in parenthesis

Can we test identifying assumption? Placebo experiment

Std. errors in parenthesis

Regression framework: Sijk : schooling of indiv i born in region j in year k Ti =1 if indiv i born in young cohort Pj : program intensity in region of birth Cj : region-specific controls Program impact given by the parameter….

Instead of “young” dummy, can estimate fully flexible form with year of birth: Sijk : schooling of indiv i born in region j in year k dil =1 if indiv i is of age l in 1974 Pj : program intensity in region of birth Cj : region-specific controls

Results (2): ln(wages) in 1995 2 x 2 table

Std. errors in parenthesis

Results (3): heterogeneous effects

Results (4): returns to education Use education regression as 1st stage for 2SLS estimate of effect of education on wages

Std. errors in parenthesis. F-stat for OID test in brackets

Bottom line

INPRES program led to +0.25 to 0.4 years of education +3% to +5.4% wages Returns to education: +6.8% to +10.6%

3. Deworming

Miguel & Kremer (Ecma, 2004)

Intestinal worm infections

Intestinal worms infect 1 in 4 people worldwide Prevalent among school-age children in developing

countries Heavy infections can lead to iron-deficiency,

anemia, malnutrition, abdominal pain Low-cost single-dose oral therapies (49 cent) can kill

the worms, reducing infections by 99 percent But reinfection is rapid, w/ worm burden returning to

80% of its original level within a year

Transmission Larvae in soil Penetrate skin Go to lungs Coughed up and

swallowed Go to intestine Eggs in feces Feces to soil …

Externalities !

Policy School children account for 85-90% of heavy

infections in Kenya Children less likely to use latrines, have poor hygiene,

contaminate each other

WHO sponsored school-based deworming programs How can we measure effectiveness?

This paper Randomized evaluation of a deworming program in

Western Kenya (Busia district) Estimate impact on school participation and

performance

Randomization strategy Suppose you go to a school and randomize among

pupils: some treated w/ deworming & others not Would you be picking up the effect of deworming by

comparing T & C pupils?

Externality: treated students are healthier less likely to infect other (untreated) students

Underestimate true effect for two reasons: Direct effect (health of treated – untreated)

underestimated b/c untreated improve Indirect effect not taken into account: positive

spillovers on untreated should be counted among the benefits of the program

SOLUTION: Group level randomization to deal with spillovers: an entire school is treated or not treated Can estimate overall effect of deworming on a school by

comparing T & C schools: this identifies the individual effect + within-school externality

Using exogenous variation in local density of treatment school pupils, we can identify between-school externality b/c composition of neighorbhood is random (e.g., distance to treatment schools)

Externality is both within school (healthy children don’t infect each other) and across schools (children w/ treated friends in neighborhood are less likely to be infected.

The Primary School Deworming Project (PSDP)

Randomized phase-in 75 primary schools, divided in 3 groups of 25.

Group 1: treated in 1998 & 1999 Group 2: treated in 1999 Group 3: treated in 2001

1998: G1=T, G2 & G3 =C 1999: G1 & G2 =T, G3 =C

Randomization check: balance table

T worse-off under-estimate effect

Randomization check: balance table

Assigned vs actual treatment

Pupils assigned to T all boys, girls under 13 (reason: birth defects if

treatment during pregnancy)

Pupils who effectively took deworming 78% of boys & girls<13

reason: school attendance on typical day = 80% 19% of girls>13

reasons: confusion about age, nurses thought benefits>costs

Solution: also use ITT

Impact on health

Regression framework

i=school, j=student, t=1,2 (year of program) T1it =1 if assigned to 1st yr of deworming treatment

Ndit = # pupils in prim. school at distance d from school i in yr t NT

dit = # of these pupils in schools assigned to treatment

: direct effect on treated + within-school externality : externalities across schools : avg effect of 1st yr treatment on treated schools

Effects on health

Separating within-school externality

D1ij =1 if indiv j in school i received treatment in 1st yr that her school was eligible

: within-school externality on untreated : within-school externality + direct effect on treated

Not possible experimentally b/c no randomization withing schools

Strategy: compare Group 1 pupils who did not receive treatment in 1998 to Group 2 pupils who did not receive treatment in 1999

Effects on education

Deworming can increase school participation Better health for pupils who were previously weak

makes attendance more worthwhile Older siblings who had to stay home when younger

ones were sick can now attend